Drug discovery remains a lengthy, costly, and high-risk endeavor, often requiring over a decade from target identification to clinical translation. Artificial intelligence (AI) is reshaping this paradigm by enabling more efficient and accurate decision-making across the discovery and development pipeline. Advances in machine learning, deep learning, and natural language processing now support target identification, hit finding, lead optimization, and drug repurposing with unprecedented speed and precision. AI-driven insilico platforms further enhance early-stage predictability by forecasting toxicity, pharmacokinetics, and developability, thereby reducing late-stage attrition. This review critically examines the evolving role of AI in modern drug discovery and its expanding impact on pharmaceutical formulation development and personalized medicine. Collaborative models between AI developers and the pharmaceutical industries, essential for accelerating translational outcomes, are also highlighted. Finally, key challenges, including algorithmic transparency, data quality, interoperability, and regulatory acceptance, are discussed, along with future directions for harnessing AI's full potential in pharmaceutics.
Ali et al. (2026) studied this question.